Platforms and Knowledge Production in the Age of A.I.
Bibliographic record
Abstract
Large-scale digital platforms designed by corporate publishers are increasingly shaping and reconfiguring all aspects of knowledge production and circulation. In the process, these platforms are reshaping the governance of academic labour in powerful but invisible ways. While designed to capitalize on the extraction, collection, and analysis of big data and their traces generated by researchers and their institutions, these platforms seek to create new markets and fashion new "values" in the forms of analytics that researchers seek. The AI in the title of this talk does not refer to Artificial Intelligence, although this is highly implicated in platform design and its logic. AI in this context refers to Automating Inequality, a term borrowed from Virginia Eubanks’ book Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. Analogous to Eubanks’ study, I argue that the predominant knowledge platform design favours the already "rich" in scholarly capital and institutional advantages and punishes the scholarly poor and those on the epistemic margins. Far from a democratizing force, open science has become a practice of complying with standards and funders’ policies and mandates, further exacerbating deep-seated structural inequalities in knowledge production. Reflecting on our many failed attempts at reclaiming the knowledge commons and co-creating open infrastructure, I call for new imaginaries and narratives of what open scholarship may look like or aspire to be. As infrastructure are fundamentally relational, we need to ask what kind of relationships we want to nourish.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.018 | 0.049 |
| Scholarly communication | 0.034 | 0.060 |
| Open science | 0.001 | 0.020 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".